DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is in response to the request for continued examination filed on 01/21/2026, in which claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 12/31/2025 has been entered.
Response to Amendment
Applicant has amended the claims to overcome the claim objections. Accordingly, the previous claim objections have been withdrawn.
Response to Arguments
Applicant’s arguments, see pages 11-20, filed 12/31/2025, with respect to the 35 U.S.C. 101 rejections have been fully considered and are persuasive. The 35 U.S.C. 101 rejections of claims 1-20 have been withdrawn.
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections of claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-6, 8-12, 14, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Xiang et al., U.S. Patent Application Publication No. 2022/0032955 A1 (hereinafter Xiang), in view of Sugimoto et al., U.S. Patent Application Publication No. 2023/0249686 A1 (hereinafter Sugimoto), and further in view of Kislovskiy et al., U.S. Patent Application Publication No. 2018/0339712 A1 (hereinafter Kislovskiy).
Regarding claim 1, Xiang teaches a behavior planning device (Xiang Fig. 2)
to receive obstacle information around a mobile object acquired by an obstacle information acquirer (see at least Xiang [0042]: “Each of the surrounding monitoring sensors 11 outputs a signal indicating the relative position, type, moving speed, etc. of each detected object as a detection result.”),
position information of the mobile object acquired by an own-position acquirer (see at least Xiang [0049]: “The locator 15 is a device that generates highly accurate position information and the like of the subject vehicle Ma by combined positioning that combines a plurality of pieces of information.”),
road information around the mobile object acquired by a road information acquirer, and obstacle information around a roadside acquired by a roadside information acquirer (see at least Xiang [0029]: “The map data corresponds to map data showing a road structure, position coordinates of feature objects arranged/disposed on the ground and along the road, and the like with an appropriate precision for automatic driving. Map data includes node data, link data, feature object data, and the like.”),
the behavior planning device comprising: an emergency stop requester to output an emergency stop request when having detected abnormality for at least one of the obstacle information around the mobile object acquired by the obstacle information acquirer and the obstacle information around the roadside acquired by the roadside information acquirer (see at least Xiang [0111]-[0112]: “If the degree of use of the abnormal sensor is equal to or higher than the risk determination threshold value, a positive determination is made in step S209, and the process proceeds to step S210…In step S210, the risk level is set to 2 and the process proceeds to step S211. In step S211 a signal requesting the safety evaluation unit F4 to perform an emergency action is output. In such manner, an emergency action such as MRM is thus performed. Since the MRM is a control for safely stopping the vehicle, step S211 corresponds to a stop processing step.”);
wherein generating the behavior plan comprises determining that the abnormality for the at least one of the obstacle information is detected based on detecting a sensor of the obstacle information acquirer or the road information acquirer as being defective (see at least Xiang [0083]: “Further, when an error signal is output from the surrounding monitoring sensor 11, the error signal can also be used as a diagnostic material. Note that determining whether or not an abnormality has occurred corresponds to detecting that an abnormality has occurred. The abnormality here refers to a state in which it (i.e., the sensor 11) is not operating normally due to some kind of malfunction. An abnormal state of the sensor 11 includes a state in which the recognition result is not output due to a failure and a state in which the output signal is stuck (e.g., stay unchanged).”)
and adjusting a route, of the mobile object and represented by the behavior plan, depending on determining a risk represented by the sensor being detected as defective (see at least Xiang [0094]: “Further, the risk response unit G6 requests the safety evaluation unit F4 to perform a predetermined emergency action based on a determination that the risk level is 2. The emergency action may be, for example, an MRM (Minimum Risk Maneuver) described below. The specific content of the MRM can be, for example, a process of autonomously driving the vehicle to a safe place and parking the vehicle while issuing an alarm to the surroundings. Safe places include road shoulders with a width equal to or greater than a predetermined value, places designated as emergency evacuation areas and the like.”; Xiang [0088]-[0089] discloses the risk level is determined based on the detected abnormality of the sensor),
and wherein the behavior planning device further comprises one or more processors configured to implement the obstacle information acquirer, the own-position acquirer, the road information acquirer, the roadside information acquirer, the emergency stop requester, the stop position determiner, and the behavior plan generator (see at least Xiang [0168]: “The control unit and the method therefor which have been described in the present disclosure may be also realized by a dedicated computer which constitutes a processor programmed to execute one or more functions realized by computer programs.”).
Xiang fails to expressly disclose determining whether the mobile object will stop in an area obstructing other traffic participants and preventing the mobile object from stopping in the area obstructing other traffic participants. However, Sugimoto teaches
a stop position determiner to determine whether or not the mobile object will stop in a first area where advancement of another traffic participant is obstructed at a time of stoppage of the mobile object, based on the road information, the position information of the mobile object, and the emergency stop request outputted from the emergency stop requester (see at least Sugimoto [0041]: “When notified that the driver’s abnormal condition is detected, the search unit 32 searches a predetermined section from the position of the vehicle 10 at detection of the abnormal condition for a first evacuation space where the vehicle 10 can stop without obstructing travel of another vehicle, by referring to the high-precision map.”);
and a behavior plan generator to, by generating a behavior plan, prevent the mobile object from stopping in the first area, in a case where the stop position determiner has determined that the mobile object will otherwise stop in the first area where advancement of another traffic participant is obstructed (see at least Sugimoto [0058]: “To achieve this, the vehicle control unit 35 generates a planned trajectory of the vehicle 10 from the current position of the vehicle 10 to the target stopping position.”; [0066]: “FIGS. 4A to 4C each illustrate a target stopping position of the vehicle 10 in the vehicle control process according to the present embodiment. In the example illustrated in FIG. 4A, there is a space 402 where the vehicle 10 can stop without obstructing travel of another vehicle, in a section 410 wherein the vehicle 10 travels a predetermined distance from an abnormality detection point 401.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the device taught by Xiang with the preventing traffic obstruction taught by Sugimoto with reasonable expectation of success. Sugimoto is directed towards the related field of a vehicle controller for emergency stop. Therefore, one of ordinary skill in the art would be motivated to combine Xiang with Sugimoto to ensure the vehicle stops appropriately when an abnormality is detected (see at least Sugimoto [0012]: “The vehicle controller according to the present disclosure has an advantageous effect of being able to stop a vehicle appropriately when a driver’s abnormal condition is detected.”).
Xiang in view of Sugimoto fails to expressly disclose adjusting a route depending on a risk for a defective sensor and traveling difficulties of candidate routes based on the sensor risk. However, Kislovskiy teaches
adjusting a route, of the mobile object and represented by the behavior plan, depending on determining both a risk, represented by the sensor being detected as defective, and, based on the risk, traveling difficulties of candidate routes of the route (see at least Kislovskiy [0086]: “The degradation level can include factors such as outdated or older sensors and hardware, older software versions, calibration faults for the vehicle's sensors (e.g., misaligned LIDAR), faulty sensors (e.g., debris or grime on a camera lens), diagnostic faults or failures, and the like. Based on the degradation level of the vehicle, and the generalized aggregate risk value 332 for the route, the risk regressor 330 can determine an individual risk value 333 for the SDAV 381 or FAV 389.”; [0062]: “In doing so, the trip classifier 250 can receive an aggregate risk value 232 for an optimal trip route 252 between the pick-up location and the destination as calculated by the risk regressor 230. As described herein, the aggregate risk value 232 can account for such factors as lane geometry, path segment complexity (e.g., bicycle lanes, intersections, crosswalks, school zones, road signage, etc.), current environmental conditions, time of day, and traffic conditions.”; [0129]: “In variations, the transport management system can first determine the aggregate risk values for each route prior to selecting a most optimal route for the trip based partially on risk.”; Kislovskiy [0146] teaches a vehicle in a degraded state including degraded sensors can be rerouted to a service station or central facility if the traffic conditions cause the overall risk to prevent the vehicle from traveling on the current path)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the device taught by Xiang in view of Sugimoto with Kislovskiy with reasonable expectation of success. Kislovskiy is directed towards the related field of an on-trip monitoring system for an on-demand transportation service. Therefore, one of ordinary skill in the art would be motivated to combine Xiang in view of Sugimoto with Kislovskiy to increase safety through dynamic risk analysis (see at least Kislovskiy [0037]: “Among other benefits, the examples described herein achieve a technical effect of safely expanding autonomous vehicle operations through dynamic risk analysis, trip classification, and robust software verification.”).
Regarding claim 2, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 1 as explained above. Sugimoto further teaches
a candidate route generator, implemented by the processor, to generate the candidate routes for the mobile object to travel, as further based on the road information and the position information of the mobile object (see at least Sugimoto [0058]: “To achieve this, the vehicle control unit 35 generates a planned trajectory of the vehicle 10 from the current position of the vehicle 10 to the target stopping position.”; [0065]: “The target stopping position may be changed on the way, e.g., in the case where an obstacle is detected in a first or second evacuation space during control of the vehicle 10 toward the evacuation space to stop the vehicle 10 there. In such a case, the vehicle control unit 35 resets the planned trajectory, depending on the changed target stopping position.”),
and the behavior plan generator generates a behavior plan for the mobile object to stop outside the first area (see at least Sugimoto [0058]: “To achieve this, the vehicle control unit 35 generates a planned trajectory of the vehicle 10 from the current position of the vehicle 10 to the target stopping position.”; [0066]: “FIGS. 4A to 4C each illustrate a target stopping position of the vehicle 10 in the vehicle control process according to the present embodiment. In the example illustrated in FIG. 4A, there is a space 402 where the vehicle 10 can stop without obstructing travel of another vehicle, in a section 410 wherein the vehicle 10 travels a predetermined distance from an abnormality detection point 401.”).
Kislovskiy further teaches
a route traveling difficulty calculator, implemented by the processor, to determine the traveling difficulties as for the mobile object to travel on each candidate route generated by the candidate route generator (see at least Kislovskiy [0126]: “Thus, for each transport request and each route, the risk regression system can determine current conditions across the a set of possible routes for the transport request (725)…The risk regression system may then execute a risk regression method using the fractional harmful event data—or conditions-based fractional risk values described herein—to determine an aggregate risk value for the route (730).”)
and further based on the obstacle information acquired by the obstacle information acquirer, the obstacle information around the roadside acquired by the roadside information acquirer, the position information of the mobile object, and abnormality information detected by the emergency stop requester, the abnormality information including the risk represented by the sensor being detected as defective (see at least Kislovskiy [0109]: “Based on the classification of the objects in the object of interest data 542, the prediction engine 545 can predict a path of each object of interest and determine whether the AV control system 520 should respond or react accordingly. For example, the prediction engine 540 can dynamically calculate a collision probability for each object of interest, and generate event alerts 551 if the collision probability exceeds a certain threshold.”; [0057]: “In various implementations, the AV software management system 200 can include a fractional harmful event quantifier 245 that can computationally analyze historical event data 244 for the given region, such as vehicle incidents and collisions, to determine a fractional risk value 247 for each path segment of the given region…As provided herein, a harmful event can correspond to physical contact between an AV and another object, such as another vehicle, a curb, a road sign, a pedestrian, and the like.”; [0086]: “For both SDAVs 381 and FAVs 389, the risk regressor 330 can determine a degradation level of the vehicle. The degradation level can include factors such as outdated or older sensors and hardware, older software versions, calibration faults for the vehicle's sensors (e.g., misaligned LIDAR), faulty sensors (e.g., debris or grime on a camera lens), diagnostic faults or failures, and the like.”),
and a behavior decider, implemented by the processor, to select a traveling route having a lowest traveling difficulty among the traveling difficulties on the candidate routes calculated by the route traveling difficulty calculator (see at least Kislovskiy [0170]: “In various examples, the transport management system can aggregate fractional risk values over a plurality of route options for the transport request to determine a least risky route (1435)…Once a most optimal driver is selected, the transport management system can transmit a transport invitation and route data to the selected driver to facilitate the trip over the least risk route (1440).”).
Regarding claim 4, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 2 as explained above. Xiang further teaches
wherein the candidate route generator generates a planned route for the mobile object to move to a target position, as one of the candidate routes, and the behavior decider preferentially selects the planned route for the mobile object to move to the target position, in a case where the traveling difficulties are the same (see at least Xiang [0095]: “For example, the risk response unit G6 outputs a signal requesting the safety evaluation unit F4 to perform the MRM. The risk response unit G6 may instruct the control planning unit F3 to generate a plan for implementing the MRM as an emergency action. The signal requesting the emergency action may be output to the control planning unit F3. The risk response unit G6 corresponds to a vehicle stop processing unit that performs a process for stopping the subject vehicle Ma.”; Xiang [0094]: Xiang teaches multiple MRM actions can be performed corresponding to the same risk level of 2, and these emergency actions include navigating to a road shoulder, navigating to an emergency evacuation area, or stopping the vehicle in the current traveling lane; therefore, preferentially selecting the planned route for the mobile object to move to the target position includes selecting a proper risk response out of all possible MRM associated with a risk level of 2; under broadest reasonable interpretation traveling difficulty includes risk).
Regarding claim 5, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 1 as explained above. Xiang further teaches
an information accumulator, implemented by the processor, to accumulate the obstacle information around the roadside acquired by the roadside information acquirer (see at least Xiang [0064]: “The map linking unit F1 outputs recognition result data identified by complementarily/compensatingly using the recognition result by the front camera 11A and the map data to the diagnostic device 30 and the fusion unit F2. For convenience, the map linking unit F1 is also described as a first recognition unit because it corresponds to a configuration in which preprocessing for the fusion unit F2 is performed, and the recognition result by the map linking unit F1 is also described as a first recognition result”),
Sugimoto further teaches
wherein in a case where the stop position determiner has determined that the mobile object will stop in the first area where advancement of another traffic participant is obstructed, the behavior plan generator generates a behavior plan for preventing the mobile object from stopping in the first area, considering also the obstacle information accumulated in the information accumulator (see at least Sugimoto [0041]: “When notified that the driver’s abnormal condition is detected, the search unit 32 searches a predetermined section from the position of the vehicle 10 at detection of the abnormal condition for a first evacuation space where the vehicle 10 can stop without obstructing travel of another vehicle, by referring to the high-precision map.”; under broadest reasonable interpretation the obstacle information accumulated includes information from the high-precision map).
Regarding claim 6, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 2 as explained above. Xiang further teaches
an information accumulator, implemented by the processor, to accumulate the obstacle information around the roadside acquired by the roadside information acquirer (see at least Xiang [0064]: “The map linking unit F1 outputs recognition result data identified by complementarily/compensatingly using the recognition result by the front camera 11A and the map data to the diagnostic device 30 and the fusion unit F2. For convenience, the map linking unit F1 is also described as a first recognition unit because it corresponds to a configuration in which preprocessing for the fusion unit F2 is performed, and the recognition result by the map linking unit F1 is also described as a first recognition result”),
Kislovskiy further teaches
wherein the route traveling difficulty calculator calculates the traveling difficulty for the mobile object to travel on each candidate route generated by the candidate route generator, considering also the obstacle information accumulated in the information accumulator (see at least Kislovskiy [0057]: “In various implementations, the AV software management system 200 can include a fractional harmful event quantifier 245 that can computationally analyze historical event data 244 for the given region, such as vehicle incidents and collisions, to determine a fractional risk value 247 for each path segment of the given region…As provided herein, a harmful event can correspond to physical contact between an AV and another object, such as another vehicle, a curb, a road sign, a pedestrian, and the like.”; [0058]: “Additionally or alternatively still, the fractional risk values 247 may be condition-specific. For example, each harmful event or close call can be correlated with a set of current conditions at the time of the event or close call. This set of current conditions can include lighting conditions, weather conditions (e.g., precipitation or fog), road conditions (e.g., wet, icy, dry, or drying), traffic conditions (e.g., other vehicles and/or pedestrian traffic), a time of day or time of week, and the like. As described below, for a given trip route 252, the current conditions 253 for the trip route 252 can be compared to the condition-depend fractional risk values 247 for the risk regressor 230 to ultimately determine the aggregate risk value 232 for the resultant trip. The fractional harmful event quantifier 245 can receive data indicating the current conditions 253 from the AV log data 291 (e.g., sensor data showing the weather and road conditions), or any number of third party resources (e.g., a live weather resource, live traffic resources, etc.).”).
Regarding claim 8, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 1 as explained above. Xiang further teaches
the obstacle information acquirer (see at least Xiang [0042]: “Each of the surrounding monitoring sensors 11 outputs a signal indicating the relative position, type, moving speed, etc. of each detected object as a detection result.”),
the own-position acquirer (see at least Xiang [0049]: “The locator 15 is a device that generates highly accurate position information and the like of the subject vehicle Ma by combined positioning that combines a plurality of pieces of information.”),
the road information acquirer, and the roadside information acquirer (see at least Xiang [0029]: “The map data corresponds to map data showing a road structure, position coordinates of feature objects arranged/disposed on the ground and along the road, and the like with an appropriate precision for automatic driving. Map data includes node data, link data, feature object data, and the like.”).
Regarding claim 9, Xiang in view of Sugimoto and Kislovskiy teach a vehicle control system (Xiang Fig. 2) comprising the behavior planning device according to claim 1 as explained above. Xiang further teaches
and a vehicle controller to control the mobile object (see at least Xiang [0053]: “The automatic drive device 20 is an ECU (Electronic Control Unit) that performs a part or all of the driving operation on behalf of an occupant in a driver's seat by controlling the travel control actuator 16 based on the detection result of the surrounding monitoring sensor 11.”).
Regarding claim 10, this claim recites a method performed by the behavior planning device of claim 1. The combination of Xiang in view of Sugimoto and Kislovskiy also teaches a method performed by the device of claim 1 as outlined in the rejection to claim 1 above. Therefore, claim 10 is rejected for the same rationale as claim 1.
Regarding claim 11, this claim recites a method for the device of claim 2 as explained above. Therefore, claim 11 is rejected for the same rationale as claim 2.
Regarding claim 12, this claim recites a method for the device of claim 5 as explained above. Therefore, claim 12 is rejected for the same rationale as claim 5.
Regarding claim 14, this claim recites a device similar to the device of claim 5 as explained above, with a dependency on claim 2. Therefore, claim 14 is rejected for the same rationale as claim 5.
Regarding claim 18, Xiang in view of Sugimoto and Kislovskiy teach a vehicle control system (Xiang Fig. 2) comprising the behavior planning device according to claim 2 as explained above. Xiang further teaches
and a vehicle controller to control the mobile object (see at least Xiang [0053]: “The automatic drive device 20 is an ECU (Electronic Control Unit) that performs a part or all of the driving operation on behalf of an occupant in a driver's seat by controlling the travel control actuator 16 based on the detection result of the surrounding monitoring sensor 11.”).
Regarding claim 19, this claim recites a method for the device of claim 5 as explained above, with a dependency on claim 11. Therefore, claim 19 is rejected for the same rationale as claim 5.
Claims 3, 13, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Xiang in view of Sugimoto and Kislovskiy, and further in view of Tsuji et al., U.S. Patent Application Publication No. 2021/0229658 A1 (hereinafter Tsuji).
Regarding claim 3, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 2 as explained above. Xiang in view of Sugimoto and Kislovskiy fail to expressly disclose calculating a high traveling difficulty when a stop position is needed before an obstacle and the stop position is determined to include an area that causes a traffic obstruction. However, Tsuji teaches
wherein the route traveling difficulty calculator in a case where an obstacle is present ahead of the first area, calculates a second area which is a stop area needed for the mobile object to stop at a position before the obstacle (see at least Tsuji [0182]-[0183]: “In step S85, the emergency stop control part 130 confirms, on the basis of output of the camera 31, the radar 32, and/or the external communication section 36, whether the stoppable region R22 is a free space, that is, whether there is a risk of collision with an on-road obstacle when the own vehicle H enters the stoppable region R22…In this situation, the determination results in NO in step S85, and the emergency stop control part 130 makes the own vehicle H travel to a location in front of the stoppable region R22 in the first-travel-lane traveling process, in step S86.”),
and in a case where at least a part of the second area is present in the first area, calculates a corresponding traveling difficulty to be high (see at least Tsuji [0183]: “In this situation, the determination results in NO in step S85, and the emergency stop control part 130 makes the own vehicle H travel to a location in front of the stoppable region R22 in the first-travel-lane traveling process, in step S86…Then, in the case in which the collision risk is determined as being the predetermined degree or higher, the emergency stop control part 130 may execute a control in accordance with the situation, such as a control to make the own vehicle H stop at the current location.”; [0272]: “The first risk value is basically set lower as the location is relatively closer to the road shoulder region”; Tsuji Fig. 12 shows a stop location associated with a traffic lane, which would obstruct advancement of another traffic participant, is associated with a higher risk value; under broadest reasonable interpretation traveling difficulty includes risk).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the device taught by Xiang in view of Sugimoto and Kislovskiy with the high difficulty taught by Tsuji with reasonable expectation of success. Tsuji is directed towards the related field of searching for a vehicle stop location using collision risk. Therefore, one of ordinary skill in the art would be motivated to combine Xiang in view of Sugimoto and Kislovskiy with Tsuji to improve safety in bringing a vehicle to an emergency stop (see at least Tsuji [0022]: “As described above, according to the technique disclosed herein, safety in bringing a vehicle to an emergency stop is improved. Moreover, it is possible to avoid elongation of time to bring the vehicle to an emergency stop.”).
Regarding claim 13, this claim recites a device similar to the device of claim 4 as explained above, with a dependency on claim 3. Therefore, claim 13 is rejected for the same rationale as claim 4.
Regarding claim 15, this claim recites a device similar to the device of claim 5 as explained above, with a dependency on claim 3. Therefore, claim 15 is rejected for the same rationale as claim 5.
Regarding claim 16, this claim recites a device similar to the device of claim 6 as explained above, with a dependency on claim 3. Therefore, claim 16 is rejected for the same rationale as claim 6.
Regarding claim 20, Xiang in view of Sugimoto, Kislovskiy, and Tsuji teach a vehicle control system (Xiang Fig. 2) comprising the behavior planning device according to claim 16 as explained above. Xiang further teaches
and a vehicle controller to control the mobile object (see at least Xiang [0053]: “The automatic drive device 20 is an ECU (Electronic Control Unit) that performs a part or all of the driving operation on behalf of an occupant in a driver's seat by controlling the travel control actuator 16 based on the detection result of the surrounding monitoring sensor 11.”).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Xiang in view of Sugimoto and Kislovskiy, and further in view of Itoh, U.S. Patent Application Publication No. 2022/0034999 A1.
Regarding claim 7, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 5 as explained above. Xiang further teaches
wherein in a case where the emergency stop requester has detected abnormality for the obstacle information around the roadside acquired by the roadside information acquirer (see at least Xiang [0102]: “Further, the abnormality detection unit G4 also performs the same processing as the sensor diagnosis process shown in FIG. 7 for the map linking unit F1 and the fusion unit F2. Thereby, from the viewpoint of the stability of the recognition result, both of the map linking unit F1 and the fusion unit F2 are subject to diagnosis as to whether each of those two units is operating normally.”)
Xiang in view of Sugimoto and Kislovskiy fails to expressly disclose generating a behavior plan for preventing the mobile object from stopping in the first area without using the obstacle information around the roadside acquired by the roadside information acquirer. However, Itoh teaches
the behavior plan generator generates a behavior plan for preventing the mobile object from stopping in the first area, using the obstacle information accumulated in the information accumulator, without using the obstacle information around the roadside acquired by the roadside information acquirer (This limitation is taught through the combination of Sugimoto and Itoh. Sugimoto teaches “the behavior plan generator generates a behavior plan for preventing the mobile object from stopping in the first area, using the obstacle information accumulated in the information accumulator” (see at least Sugimoto [0041], [0058]). Sugimoto fails to expressly disclose “without using the obstacle information around the roadside acquired by the roadside information acquirer” that has a detected abnormality. However, Itoh teaches “without using the obstacle information around the roadside acquired by the roadside information acquirer” which has a detected abnormality (see at least Itoh [0030]: “Further, the result of the determination in the abnormality determination processing unit 13 is transmitted to the process block in the host system 14 (not shown) and the host system 14 performs maintenance processing such as stopping the infrastructure sensor apparatus 10 or invalidating mobile body information obtained in the infrastructure sensor apparatus 10 in accordance with the result of the determination in the abnormality determination processing unit 13.”; the obstacle information around the roadside is not used when an abnormality is detected because Itoh teaches stopping an infrastructure sensor apparatus when an abnormality is determined). Therefore, the combination of Sugimoto and Itoh teach the entirety of this limitation) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the device taught by Xiang in view of Sugimoto and Kislovskiy with the not using information based on an abnormality taught by Itoh with reasonable expectation of success. Itoh is directed towards the related field of abnormality detection of an infrastructure sensor. Therefore, one of ordinary skill in the art would be motivated to combine Xiang in view of Sugimoto and Kislovskiy with Itoh to improve reliability of information (see at least Itoh [0006]: “The present disclosure has been made in order to solve the above problem, and aims to detect an abnormality that has occurred in an infrastructure sensor apparatus and improve reliability of information obtained from the infrastructure sensor apparatus.”).
Claim 17 is rejected under 35 U.S.C. 103 as being unpatentable over Xiang in view of Sugimoto and Kislovskiy, and further in view of Itoh.
Regarding claim 17, Xiang in view of Sugimoto and Kislovskiy teach all elements of the behavior planning device according to claim 14 as explained above. Xiang further teaches
wherein in a case where the emergency stop requester has detected abnormality for the obstacle information around the roadside acquired by the roadside information acquirer (see at least Xiang [0102]: “Further, the abnormality detection unit G4 also performs the same processing as the sensor diagnosis process shown in FIG. 7 for the map linking unit F1 and the fusion unit F2. Thereby, from the viewpoint of the stability of the recognition result, both of the map linking unit F1 and the fusion unit F2 are subject to diagnosis as to whether each of those two units is operating normally.”)
Xiang in view of Sugimoto and Kislovskiy fails to expressly disclose generating a behavior plan for preventing the mobile object from stopping in the first area without using the obstacle information around the roadside acquired by the roadside information acquirer. However, Itoh teaches
the behavior plan generator generates a behavior plan for preventing the mobile object from stopping in the first area, using the obstacle information accumulated in the information accumulator, without using the obstacle information around the roadside acquired by the roadside information acquirer (This limitation is taught through the combination of Sugimoto and Itoh. Sugimoto teaches “the behavior plan generator generates a behavior plan for preventing the mobile object from stopping in the first area, using the obstacle information accumulated in the information accumulator” (see at least Sugimoto [0041], [0058]). Sugimoto fails to expressly disclose “without using the obstacle information around the roadside acquired by the roadside information acquirer” that has a detected abnormality. However, Itoh teaches “without using the obstacle information around the roadside acquired by the roadside information acquirer” which has a detected abnormality (see at least Itoh [0030]: “Further, the result of the determination in the abnormality determination processing unit 13 is transmitted to the process block in the host system 14 (not shown) and the host system 14 performs maintenance processing such as stopping the infrastructure sensor apparatus 10 or invalidating mobile body information obtained in the infrastructure sensor apparatus 10 in accordance with the result of the determination in the abnormality determination processing unit 13.”; the obstacle information around the roadside is not used when an abnormality is detected because Itoh teaches stopping an infrastructure sensor apparatus when an abnormality is determined). Therefore, the combination of Sugimoto and Itoh teach the entirety of this limitation) .
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to modify the device taught by Xiang in view of Sugimoto and Kislovskiy with the not using information based on an abnormality taught by Itoh with reasonable expectation of success. Itoh is directed towards the related field of abnormality detection of an infrastructure sensor. Therefore, one of ordinary skill in the art would be motivated to combine Xiang in view of Sugimoto and Kislovskiy with Itoh to improve reliability of information (see at least Itoh [0006]: “The present disclosure has been made in order to solve the above problem, and aims to detect an abnormality that has occurred in an infrastructure sensor apparatus and improve reliability of information obtained from the infrastructure sensor apparatus.”).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Segawa et al., U.S. Patent Application Publication No. 2018/0232967 A1, directed towards processing derivative information including sensor abnormality information and travel difficulty information.
Oh et al., U.S. Patent Application Publication No. 2016/0025505 A1, directed towards evaluating driving difficulty of candidate paths based on sensor recognition rates.
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/ELIZABETH J SLOWIK/ Examiner, Art Unit 3662
/ANISS CHAD/ Supervisory Patent Examiner, Art Unit 3662